Senior Product Manager - AI-2

Momentive Software•Atlanta, GA

7d•Remote

About The Position

We are seeking a Senior Product Manager with a strong focus on AI to leverage AI tools as core infrastructure in daily work, acting as force multipliers for research, analysis, communication, and decision-making. This role involves compressing discovery and validation cycles using AI to synthesize customer feedback, analyze competitive landscapes, and generate initial product artifacts. The ideal candidate will architect their own analysis pipelines, continuously evolve their AI toolkit, and prototype/validate product concepts rapidly. A deep fluency in the AI product landscape, understanding of foundation models, and experience with AI product patterns like RAG, agentic workflows, embeddings, fine-tuning, and evaluation frameworks are crucial. The role requires owning and communicating the strategic roadmap for an assigned product area, identifying where AI creates transformative value, and translating customer signals into product decisions. The Senior Product Manager will lead the full product lifecycle, from discovery to iteration, and own execution with engineering teams, while partnering with GTM teams for successful product launches and adoption. This position emphasizes iterative shipping, establishing feedback loops with users, and using data to inform product development. Other duties as assigned.

Requirements

Bachelor's degree or equivalent work experience.
5+ years of product management experience in a software/SaaS environment.
Demonstrated, meaningful use of AI tools in your own PM workflow, with concrete examples of how AI has changed your work.
Experience building or shipping product capabilities that incorporate AI/ML, with at least one production deployment preferred.
Experience working with engineering teams on AI/ML systems (model integration, context engineering, evaluation pipelines, data quality).
Working understanding of foundation models, their capabilities, limitations, and appropriate use cases.
Familiarity with AI product patterns: RAG, agentic workflows, embeddings, fine-tuning, and evaluation frameworks.
Understanding of APIs, cloud infrastructure, data pipelines, and modern development practices.
Comfort using AI-assisted development and prototyping tools (e.g., Claude, Cursor, or similar) to validate concepts and accelerate decision-making.
Revenue fluency: ability to read customer signals as product priorities with dollar values attached, including churn risk, deal blockers, and expansion opportunities.
Strong analytical skills with experience using data to drive prioritization, measure outcomes, and build business cases.
Excellent written and verbal communication, with the ability to produce clear product specs, strategy documents, and stakeholder communications.
Ability to lead cross-functional teams through influence, not authority.
Comfort operating with high autonomy and high accountability, owning outcomes, not just activities.

Nice To Haves

Background in the Association or Non-Profit space is a bonus.

Responsibilities

Use AI tools as core infrastructure in daily work for research, analysis, communication, and decision-making.
Compress discovery and validation cycles by using AI to synthesize customer feedback, analyze competitive landscapes, and generate first-draft artifacts.
Architect analysis pipelines using AI to query and synthesize large datasets, extract patterns from user interviews, and surface insights.
Continuously evolve AI toolkit and share effective patterns with the broader product organization.
Prototype and validate product concepts rapidly, arriving at planning discussions with working proofs-of-concept.
Maintain deep fluency in the AI product landscape, including competitors, customer adoption, and technology trends.
Own and communicate the strategic roadmap for assigned product areas, identifying AI's transformative value and prioritizing accordingly.
Think architecturally to build capabilities that solve multiple problems and expand the product's surface area.
Translate quantified customer signals (revenue impact, churn risk, deal blockers) into product decisions.
Lead the full product lifecycle: discovery, definition, development, evaluation, launch, and iteration.
Own execution with engineering teams.
Partner with GTM teams to ensure product capabilities are positioned, launched, and adopted.
Ship iteratively, establishing feedback loops with real users early and often, and using data to inform the next cycle.